Road Extraction from Remote Sensing Images using Hough Transform in MATLAB

In remote sensing image processing, road extraction represents a critical task where Hough Transform is widely employed due to its robust line detection capabilities. MATLAB offers comprehensive image processing toolboxes that enable efficient implementation of Hough Transform-based road extraction algorithms. The core concept of Hough Transform involves mapping lines from image space to parameter space (typically polar coordinates), where peak points are identified through accumulator array statistics to detect straight lines in the image. This method demonstrates strong robustness against noise and partial occlusions, making it particularly suitable for extracting regular road patterns in remote sensing imagery. The implementation approach generally follows these key steps: First, preprocess the remote sensing image through operations like grayscale conversion, edge enhancement, or binarization using functions such as rgb2gray() and imbinarize(). Then apply edge detection algorithms like Canny edge detection (edge() function with 'Canny' method) to extract road boundaries. Subsequently, implement Hough Transform using functions like hough() and houghpeaks() to detect line segments in the edge map. Finally, perform post-processing operations including line segment merging and filtering using houghlines() function to optimize extraction results. It's important to note that while Hough Transform effectively detects straight road segments, curved road extraction may require algorithmic improvements or integration with other methods. Additionally, parameter selection (such as accumulator threshold values in houghpeaks()) significantly impacts detection accuracy and requires experimental tuning through parameter optimization loops.